{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import matplotlib.dates as mdates\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import norm\n",
    "from sympy import Symbol, symbols, Matrix, sin, cos\n",
    "from sympy import init_printing\n",
    "init_printing(use_latex=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Extended Kalman Filter Implementation for Constant Heading and Velocity (CHCV) Vehicle Model in Python\n",
    "\n",
    "![Extended Kalman Filter Step](Extended-Kalman-Filter-Step.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "[Wikipedia](http://en.wikipedia.org/wiki/Extended_Kalman_filter) writes: In the extended Kalman filter, the state transition and observation models need not be linear functions of the state but may instead be differentiable functions.\n",
    "\n",
    "$\\boldsymbol{x}_{k} = g(\\boldsymbol{x}_{k-1}, \\boldsymbol{u}_{k-1}) + \\boldsymbol{w}_{k-1}$\n",
    "\n",
    "$\\boldsymbol{z}_{k} = h(\\boldsymbol{x}_{k}) + \\boldsymbol{v}_{k}$\n",
    "\n",
    "Where $w_k$ and $v_k$ are the process and observation noises which are both assumed to be zero mean Multivariate Gaussian noises with covariance matrix $Q$ and $R$ respectively.\n",
    "\n",
    "The function $g$ can be used to compute the predicted state from the previous estimate and similarly the function $h$ can be used to compute the predicted measurement from the predicted state. However, $g$ and $h$ cannot be applied to the covariance directly. Instead a matrix of partial derivatives (the Jacobian matrix) is computed.\n",
    "\n",
    "At each time step, the Jacobian is evaluated with current predicted states. These matrices can be used in the Kalman filter equations. This process essentially linearizes the non-linear function around the current estimate."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## State Vector - Constant Heading and Velocity Model (CHCV)\n",
    "\n",
    "Situation covered: You have a position sensor, which measures the vehicle position ($x$ & $y$) and you try to estimate the heading and the velocity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Constant Heading, Constant Velocity Model\n",
    "\n",
    "$$ x_k= \\begin{bmatrix} x^{*} \\\\ y^{*} \\\\ \\psi \\\\ v \\end{bmatrix} = \\begin{bmatrix} \\text{Position X} \\\\ \\text{Position Y} \\\\ \\text{Heading} \\\\ \\text{Velocity} \\end{bmatrix} $$\n",
    "\n",
    "$^{*}$=actually measured values in this implementation example!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "numstates=4 # States"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Developing the math behind dynamic model\n",
    "\n",
    "All symbolic calculations are made with [Sympy](http://nbviewer.ipython.org/github/jrjohansson/scientific-python-lectures/blob/master/Lecture-5-Sympy.ipynb). Thanks!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "vs, psis, dts, xs, ys, lats, lons = symbols('v \\psi T x y lat lon')\n",
    "\n",
    "gs = Matrix([[xs+vs*dts*cos(psis)],\n",
    "             [ys+vs*dts*sin(psis)],\n",
    "             [psis],\n",
    "             [vs]])\n",
    "state = Matrix([xs,ys,psis,vs])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Dynamic Function $g$\n",
    "\n",
    "This formulas calculate how the state is evolving from one to the next time step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAJAAAABkCAMAAABem0mFAAAAP1BMVEX///8AAAAAAAAAAAAAAAAA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\nxyJD+RSzpIYAAAAASUVORK5CYII=\n",
      "text/latex": [
       "$$\\left[\\begin{matrix}T v \\cos{\\left (\\psi \\right )} + x\\\\T v \\sin{\\left (\\psi \\right )} + y\\\\\\psi\\\\v\\end{matrix}\\right]$$"
      ],
      "text/plain": [
       "⎡T⋅v⋅cos(\\psi) + x⎤\n",
       "⎢                 ⎥\n",
       "⎢T⋅v⋅sin(\\psi) + y⎥\n",
       "⎢                 ⎥\n",
       "⎢      \\psi       ⎥\n",
       "⎢                 ⎥\n",
       "⎣        v        ⎦"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Calculate the Jacobian of the Dynamic function $g$ with respect to the state vector $x$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAB4AAABkCAMAAACmV3A3AAAAP1BMVEX///8AAAAAAAAAAAAAAAAA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\n",
      "text/latex": [
       "$$\\left[\\begin{matrix}x\\\\y\\\\\\psi\\\\v\\end{matrix}\\right]$$"
      ],
      "text/plain": [
       "⎡ x  ⎤\n",
       "⎢    ⎥\n",
       "⎢ y  ⎥\n",
       "⎢    ⎥\n",
       "⎢\\psi⎥\n",
       "⎢    ⎥\n",
       "⎣ v  ⎦"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAARwAAABkCAMAAABjLH/KAAAAP1BMVEX///8AAAAAAAAAAAAAAAAA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\ndUn2hOtnAAAAAElFTkSuQmCC\n",
      "text/latex": [
       "$$\\left[\\begin{matrix}1 & 0 & - T v \\sin{\\left (\\psi \\right )} & T \\cos{\\left (\\psi \\right )}\\\\0 & 1 & T v \\cos{\\left (\\psi \\right )} & T \\sin{\\left (\\psi \\right )}\\\\0 & 0 & 1 & 0\\\\0 & 0 & 0 & 1\\end{matrix}\\right]$$"
      ],
      "text/plain": [
       "⎡1  0  -T⋅v⋅sin(\\psi)  T⋅cos(\\psi)⎤\n",
       "⎢                                 ⎥\n",
       "⎢0  1  T⋅v⋅cos(\\psi)   T⋅sin(\\psi)⎥\n",
       "⎢                                 ⎥\n",
       "⎢0  0        1              0     ⎥\n",
       "⎢                                 ⎥\n",
       "⎣0  0        0              1     ⎦"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs.jacobian(state)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "It has to be computed on every filter step because it consists of state variables!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Initial Uncertainty $P_0$\n",
    "\n",
    "Initialized with $0$ means you are pretty sure where the vehicle starts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1000.     0.     0.     0.]\n",
      " [    0.  1000.     0.     0.]\n",
      " [    0.     0.  1000.     0.]\n",
      " [    0.     0.     0.  1000.]] (4, 4)\n"
     ]
    }
   ],
   "source": [
    "P = np.eye(numstates)*1000.0\n",
    "print(P, P.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Process Noise Covariance Matrix $Q$\n",
    "\n",
    "\"*The state uncertainty model models the disturbances which excite the linear system. Conceptually, it estimates how bad things can get when the system is run open loop for a given period of time.*\" - Kelly, A. (1994). A 3D state space formulation of a navigation Kalman filter for autonomous vehicles, (May). Retrieved from http://oai.dtic.mil/oai/oai?verb=getRecord&metadataPrefix=html&identifier=ADA282853\n",
    "\n",
    "Because this depends on the `dt`, it has to be calculated in every filterstep."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Real Measurements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Read '2014-03-26-000-Data.csv' successfully.\n"
     ]
    }
   ],
   "source": [
    "#path = './../RaspberryPi-CarPC/TinkerDataLogger/DataLogs/2014/'\n",
    "datafile = '2014-03-26-000-Data.csv'\n",
    "\n",
    "date, \\\n",
    "time, \\\n",
    "millis, \\\n",
    "ax, \\\n",
    "ay, \\\n",
    "az, \\\n",
    "rollrate, \\\n",
    "pitchrate, \\\n",
    "yawrate, \\\n",
    "roll, \\\n",
    "pitch, \\\n",
    "yaw, \\\n",
    "speed, \\\n",
    "course, \\\n",
    "latitude, \\\n",
    "longitude, \\\n",
    "altitude, \\\n",
    "pdop, \\\n",
    "hdop, \\\n",
    "vdop, \\\n",
    "epe, \\\n",
    "fix, \\\n",
    "satellites_view, \\\n",
    "satellites_used, \\\n",
    "temp = np.loadtxt(datafile, delimiter=',', unpack=True,skiprows=1)\n",
    "\n",
    "print('Read \\'%s\\' successfully.' % datafile)\n",
    "\n",
    "# A course of 0° means the Car is traveling north bound\n",
    "# and 90° means it is traveling east bound.\n",
    "# In the Calculation following, East is Zero and North is 90°\n",
    "# We need an offset.\n",
    "course =(-course+90.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate `dt` for Measurements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dt = np.hstack([0.02, np.diff(millis)])/1000.0 # in s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Measurement Function $h$\n",
    "\n",
    "Matrix $J_H$ is the Jacobian of the Measurement function $h$ with respect to the state. Function $h$ can be used to compute the predicted measurement from the predicted state.\n",
    "\n",
    "If a GPS measurement is available, the following function maps the state to the measurement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABgAAAAyBAMAAAC0bgabAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMA74lUMhB2qyJEZt3N\nu5lcG7FTAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyElEQVQoFWOQ//+JAQz0/39kEHZxhXBCXAwZ\nRCBMEOlIHU5YemdSAtQ0VgWubdwHoBymAKavzA0wGQaeC0A7YZbyJyBx/B3gHOaAfAa2AKiy+Qb3\nGbqAUmCHhqeFvVoA44DUgwCVvAAxbABNY81LYGiEeZuJWYAhH+a5Zr4DDHdhnADGBIZvMA7D/AaW\nL3DOGwY2ATjnOgNvAZzzlmH+AjgnXO19A5zDwPAGyIZ4m/0A6084h3sBbwKcw5n2BMTGGzooSQw5\n8QEALm87XfZ6oCEAAAAASUVORK5CYII=\n",
      "text/latex": [
       "$$\\left[\\begin{matrix}x\\\\y\\end{matrix}\\right]$$"
      ],
      "text/plain": [
       "⎡x⎤\n",
       "⎢ ⎥\n",
       "⎣y⎦"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hs = Matrix([[xs],\n",
    "             [ys]])\n",
    "hs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAHQAAAAyBAMAAAB/teJlAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\n4UGEy+L899xXDvfxUxv/V/obkl2sEOvQAmkAAAAASUVORK5CYII=\n",
      "text/latex": [
       "$$\\left[\\begin{matrix}1 & 0 & 0 & 0\\\\0 & 1 & 0 & 0\\end{matrix}\\right]$$"
      ],
      "text/plain": [
       "⎡1  0  0  0⎤\n",
       "⎢          ⎥\n",
       "⎣0  1  0  0⎦"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "JHs=hs.jacobian(state)\n",
    "JHs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "If no GPS measurement is available, simply set the corresponding values in $J_h$ to zero."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Measurement Noise Covariance $R$\n",
    "\n",
    "\"In practical use, the uncertainty estimates take on the significance of relative weights of state estimates and measurements. So it is not so much important that uncertainty is absolutely correct as it is that it be relatively consistent across all models\" - Kelly, A. (1994). A 3D state space formulation of a navigation Kalman filter for autonomous vehicles, (May). Retrieved from http://oai.dtic.mil/oai/oai?verb=getRecord&metadataPrefix=html&identifier=ADA282853"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 36.   0.]\n",
      " [  0.  36.]] (2, 2)\n"
     ]
    }
   ],
   "source": [
    "varGPS = 6.0 # Standard Deviation of GPS Measurement\n",
    "R = np.diag([varGPS**2.0, varGPS**2.0])\n",
    "\n",
    "print(R, R.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Identity Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.  0.  0.  0.]\n",
      " [ 0.  1.  0.  0.]\n",
      " [ 0.  0.  1.  0.]\n",
      " [ 0.  0.  0.  1.]] (4, 4)\n"
     ]
    }
   ],
   "source": [
    "I = np.eye(numstates)\n",
    "print(I, I.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "## Approx. Lat/Lon to Meters to check Location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "RadiusEarth = 6378388.0 # m\n",
    "arc= 2.0*np.pi*(RadiusEarth+altitude)/360.0 # m/°\n",
    "\n",
    "dx = arc * np.cos(latitude*np.pi/180.0) * np.hstack((0.0, np.diff(longitude))) # in m\n",
    "dy = arc * np.hstack((0.0, np.diff(latitude))) # in m\n",
    "\n",
    "mx = np.cumsum(dx)\n",
    "my = np.cumsum(dy)\n",
    "\n",
    "ds = np.sqrt(dx**2+dy**2)\n",
    "\n",
    "GPS=(ds!=0.0).astype('bool') # GPS Trigger for Kalman Filter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Initial State"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.        ]\n",
      " [ 0.        ]\n",
      " [ 1.57079633]\n",
      " [ 0.        ]] (4, 1)\n"
     ]
    }
   ],
   "source": [
    "x = np.matrix([[mx[0], my[0], 0.5*np.pi, 0.0]]).T\n",
    "print(x, x.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### Put everything together as a measurement vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 10800)\n"
     ]
    }
   ],
   "source": [
    "measurements = np.vstack((mx, my))\n",
    "# Lenth of the measurement\n",
    "m = measurements.shape[1]\n",
    "print(measurements.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "# Preallocation for Plotting\n",
    "x0, x1, x2, x3 = [], [], [], []\n",
    "Zx, Zy = [], []\n",
    "Px, Py, Pdx, Pdy = [], [], [], []\n",
    "Kx, Ky, Kdx, Kdy = [], [], [], []\n",
    "\n",
    "def savestates(x, Z, P, K):\n",
    "    x0.append(float(x[0]))\n",
    "    x1.append(float(x[1]))\n",
    "    x2.append(float(x[2]))\n",
    "    x3.append(float(x[3]))\n",
    "    Zx.append(float(Z[0]))\n",
    "    Zy.append(float(Z[1]))    \n",
    "    Px.append(float(P[0,0]))\n",
    "    Py.append(float(P[1,1]))\n",
    "    Pdx.append(float(P[2,2]))\n",
    "    Pdy.append(float(P[3,3]))\n",
    "    Kx.append(float(K[0,0]))\n",
    "    Ky.append(float(K[1,0]))\n",
    "    Kdx.append(float(K[2,0]))\n",
    "    Kdy.append(float(K[3,0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Extended Kalman Filter\n",
    "\n",
    "![Extended Kalman Filter Step](Extended-Kalman-Filter-Step.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "$$x_k= \\begin{bmatrix} x \\\\ y \\\\ \\psi \\\\ v \\end{bmatrix} = \\begin{bmatrix} \\text{Position X} \\\\ \\text{Position Y} \\\\ \\text{Heading} \\\\ \\text{Velocity}  \\end{bmatrix} =  \\underbrace{\\begin{bmatrix}x[0] \\\\ x[1] \\\\ x[2] \\\\ x[3]   \\end{bmatrix}}_{\\textrm{Python Nomenclature}}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "for filterstep in range(m):\n",
    "    \n",
    "    # Time Update (Prediction)\n",
    "    # ========================\n",
    "    # Project the state ahead\n",
    "    # see \"Dynamic Matrix\"\n",
    "    x[0] = x[0] + dt[filterstep]*x[3]*np.cos(x[2])\n",
    "    x[1] = x[1] + dt[filterstep]*x[3]*np.sin(x[2])\n",
    "    x[2] = (x[2]+ np.pi) % (2.0*np.pi) - np.pi\n",
    "    x[3] = x[3]\n",
    "    \n",
    "    # Calculate the Jacobian of the Dynamic Matrix A\n",
    "    # see \"Calculate the Jacobian of the Dynamic Matrix with respect to the state vector\"\n",
    "    a13 = -dt[filterstep]*x[3]*np.sin(x[2])\n",
    "    a14 = dt[filterstep]*np.cos(x[2])\n",
    "    a23 = dt[filterstep]*x[3]*np.cos(x[2])\n",
    "    a24 = dt[filterstep]*np.sin(x[2])\n",
    "    JA = np.matrix([[1.0, 0.0, a13, a14],\n",
    "                    [0.0, 1.0, a23, a24],\n",
    "                    [0.0, 0.0, 1.0, 0.0],\n",
    "                    [0.0, 0.0, 0.0, 1.0]])\n",
    "    \n",
    "    \n",
    "    # Calculate the Process Noise Covariance Matrix\n",
    "    sGPS     = 0.5*8.8*dt[filterstep]**2  # assume 8.8m/s2 as maximum acceleration\n",
    "    sCourse  = 2.0*dt[filterstep] # assume 0.5rad/s as maximum turn rate\n",
    "    sVelocity= 35.0*dt[filterstep] # assume 8.8m/s2 as maximum acceleration\n",
    "\n",
    "    Q = np.diag([sGPS**2, sGPS**2, sCourse**2, sVelocity**2])\n",
    "    \n",
    "    # Project the error covariance ahead\n",
    "    P = JA*P*JA.T + Q\n",
    "    \n",
    "    # Measurement Update (Correction)\n",
    "    # ===============================\n",
    "    # Measurement Function\n",
    "    hx = np.matrix([[float(x[0])],\n",
    "                    [float(x[1])]])\n",
    "\n",
    "    if GPS[filterstep]: # with 10Hz, every 5th step\n",
    "        JH = np.matrix([[1.0, 0.0, 0.0, 0.0],\n",
    "                        [0.0, 1.0, 0.0, 0.0]])\n",
    "    else: # every other step\n",
    "        JH = np.matrix([[0.0, 0.0, 0.0, 0.0],\n",
    "                        [0.0, 0.0, 0.0, 0.0]])        \n",
    "    \n",
    "    S = JH*P*JH.T + R\n",
    "    K = (P*JH.T) * np.linalg.inv(S)\n",
    "\n",
    "    # Update the estimate via\n",
    "    Z = measurements[:,filterstep].reshape(JH.shape[0],1)\n",
    "    y = Z - (hx)                         # Innovation or Residual\n",
    "    x = x + (K*y)\n",
    "\n",
    "    # Update the error covariance\n",
    "    P = (I - (K*JH))*P\n",
    "\n",
    "    # Save states for Plotting\n",
    "    savestates(x, Z, P, K)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Lets take a look at the filter performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "def plotP():\n",
    "    fig = plt.figure(figsize=(16,9))\n",
    "    plt.semilogy(range(m),Px, label='$x$')\n",
    "    plt.step(range(m),Py, label='$y$')\n",
    "    plt.step(range(m),Pdx, label='$\\psi$')\n",
    "    plt.step(range(m),Pdy, label='$v$')\n",
    "\n",
    "    plt.xlabel('Filter Step')\n",
    "    plt.ylabel('')\n",
    "    plt.title('Uncertainty (Elements from Matrix $P$)')\n",
    "    plt.legend(loc='best',prop={'size':22})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Uncertainties in $P$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Y1NSQZVdykkORAACA9iBxBQD0eNYXOs/27vvvdygSAADQHiSuAIAer2bdupDl\nig8+dCgSAADQHiSuAIAeb98//ul0CAAAoANIXAEAAAAAUS0qE9fuNI8rs98AQPcRN3Ro4HHVN984\nGAkAAGiLqExcu8M8rsYVfhThhnMFAgCiR6/JkwOPy957z8FIAABAW0Rl4todjd/mlSTt+r8/OBwJ\nAKBJQd1kTFycg4EAAIC2IHGNkMEF/sTVV1bmcCQAgGC2tjZ4KfDIxJK4AgDQXZC4RshT0xIlSWlT\npjgcCQAgmHW7A4+TDj888NiVmOhEOAAAoB1IXCOkIINDCQDRyPp8gcdpU6cGHsdkZToRDgAAaAey\nrQix9WM1WV+z5QAAXcuzd2/gsXHt/9qr+PBDJ8IBAADtQOIaIfWJa/CVfQCA83b87MqQ5YQxYyRJ\nJa+/4UQ4AACgHUhcI8Rn6jJXHxO7AkA08Xz7bchy1tW/dCgSAADQXiSuHZRYs0eSVOWtktRw9EoA\nQLSxNXxOAwDQ3ZC4tpORv4X16DV/kPFUy1fX4FrwwAMORgUAaEnSkUdIkmL793c4EgAA0FpRmbga\nY6YaY54sKSlxOpRWmfbuI/sHZwIARLWEESMU26+fYvv0cToUAADQSlGZuFpr51lrr0pPT3c6lFYx\nkjwxTkcBAGhOv1tvCTz2FBSoes0a+WpqHIwIAAC0VlQmrt2RJ9aodugAxWVnOx0KACCMxIMPbrTO\nuj0ORAIABy5vWZlyxo1X2eLFToeCbobEtb3CzNfqGdpfJjnJgWCAnslTVCRrGakbHdPr1FOk2Fgl\nH3OM06EAwAGvvG4O7T1/ftThSNDdkLi2k3FXOR0C0KPVbNqkjd87Vvv++U+nQ0E3V754ieTxyJj9\ngxEkjB/vYEQAcOCqn6Is4aCDHI4E3Q2JazsZb3XjdTVu1W7a7EA0QM9T+cUXkqTyDz5wOBL0ROlT\np9Y9okUfALpS7fYdkqS4QYMcjgTdDYlrOyVlN75an/T5WklS1apVXR0O0OPYKn+vBndunsORoLtL\nPOQQpZx4Qsi6mvXrJUnFL77oREgAcMCq/PxzSVLFxx+resMGh6NBd0Li2k5JWUO0KWZU2OdMXFwX\nRwP0QDGxkiTP7t0OB4Juz9rA3Nv1yt55R5JU/n7TLfq+6mrVbNzYqaEBiKzShYtUs2WL02GgGfWj\nuVevXaudN9/scDToTkhcO4GJj3c6BKDbMzF8PCEyrKzkCj2fko48UpKU/J3vNLnd7j/cpy1Tp8lX\nUdGp8QGInJ033KAtU852Ogw0J2jQxfpb7NwFBapeu9apiNBN8Mswgvbe+iP/A1/jEYcBtI0rOTnw\n2L27wMFI0O35rGRCW1z73/mGpXmaAAAgAElEQVQbSVLt9u1NblY8d64kybrdnRcbgIjxVTU/cGbl\nypUq/+STLooGTfJ6Aw/rG3s2nXCitl5woVMRoZsgcY0gG1/XtbGw0OFIgB4gKNHw7PrWwUDQ7Vl/\n4rp6Z4nueG2VrLVyJadIkkrffNPh4ABESm1ubrPPb7/sR8q98uddFA2a4tmzJ/A4Ydw4ByNBd0Pi\n2gGjvQ1GEK77oZ1/++0ORAP0MEFdibylZQ4Ggm7PWhmX0RVPL9ecz3ZoX0Vtm7qiV65c2YnBAYgU\nd95OSVLsgAEOR4LWcqUkhyzvffIfDkWC7oDENYJchXslSZ58WoeAjrJBiWv+rbc6GAm6PeuTZOSr\nO6eMJJOY1OrNd//pT50TF4CIsrW1kqSY1F4OR4KmeEtLQ5Yrly5T3k03BZb3zJ7d1SGhG4nKxNUY\nM9UY82RJSYnTobSJ2ZXjdAhAzxE0vaa3uNi5ONDt2bquwvWnlDFGMb1SAs/XbtvW7Pbxw4Z1XnAA\nIqb+nvWYjN6SJG95hWxtrUrmval1hx/hZGio485rPMVd2fwFDkSC7ijW6QDCsdbOkzRv0qRJv3A6\nlraomNhPma9uUvzw4U6HAnR/QS2uQIdYSS5X4JRyhY7TJF9NbbObVyxd1jlxAYgYb2mp9jz8sCTJ\nlZYma602TJrkcFRoiFt/0BFR2eLaXdmEGEktX70H2qo2Ly+k6+wB4UB7veg8Pp9kFNRVODRzbXLu\n7fopdBhVGIh6tVu3Bh6XL1mioudfaLJs1ddfq7RuLmfJPxox0151DU8Bc7Oj/UhcI4of2oi8siVL\ntPnU01Tyyivt2r6nJL3u3XzZoZ2slTFm/0d0Xd6aNHGiJGn3//1f+O2Y2gzoNormzAlZ3v2HPzRZ\ndtv0H2rnddcHbkNZf+RErT+K1tmuULuj+ZGfgeaQuHYST1GR0yGgh6jZskWStO/pZ9q8bfknn2jz\nqaep6NlnQ9bn3XiTdv3hvojE13lCk+3iF190KA50e9ZKxhVoca2XdtZZkqSKTz9tsYqaTZs6JTQA\nkVHy+htt38jlUsm88FNi1Wze3CMu+kYbbyvGrwmeLgcIRuIaST5P4GHRC3OaKQi0nnHVdUGvS2Db\nombDRklS9drQgcPKFixQ0XPPdTy4ztTgB0Px//7nUCDo7tz5+ZIx8tWfUnX/95aVNrmNJCl2/zAQ\n1uvtnOAAOGbDd74bMmp9zrjxqly5UhWfLdeWs8/R+olHKe/Gm1Q0d65K5s1zMNIexFq5kpM1/JWX\nmy4S5vPWer3y1TY/HgF6PhLXSErMCDysXr3awUDQo7j2/5m2dPW3ZstWVa9dG1iuT05j+/YJrNv7\nj+4xR1r9a02cMEGSf5opS9dNtIOV/wp+ldv/Y6i+5TXjwgsDZRpO0SBJ8uy/GFmzYUOnxgigZb7a\nWn17112NRpqPZMto8UtzVfWFf+5mW1WlsgULtOu3v1P+rbdFbB8HEl91tcrefz+wXLXqG5mEBCUd\ncojSz50WdptwLa47b7pZ235wUWeFiW6CxDWC9pTXSMZ/81R50B9pexQ88ojKP/wwAlGh2/Pu//Hs\n3bev2aJbpkzR1gv2/xh35+dLkgrn/Me/fUmJ9jzUTeZIq/sh0veGGwKrajdvdioadGdutxLGjAks\n/nmJvydCXP/+gXXFr7wasknDiyT8aAWcYT2ewO1Xhf/6t4r/+6J2/3GWJGnfv/6lnHHjtW78wRHb\nX/W6darNbTxliyT5amoitp/urmLpUuWMG6+ccePlq6xsstzuP85S3tW/UtUqf4OOZ3eBFOPvSZb1\ny6sD5freeGPg8bZLL2tUT9miRR2+gBhN75+trW3XBZct552v3F9f2wkRdQ8krhHUd/OrGhg0yEdH\nRqjb9/gTyv3VNZEIC91cbNCP643HTw7MU9cc6/HIBo2Eauu+VBoOihDV92LXd+cs3X8/zJap4a/O\ndneeFi5INOSrqlJND07iv73rLq079LCItKJUrvS3nBQ9/3xg3Vurvm1ULvg886/wt87GZGV1OIYD\nUc6EQ7XjZz9zOgx0c559+7RuwqHa+L1jlTNuvKzH/71W8vrryhk3XgWz7m9TfX2uafl3Vc369Sp5\n9dWwz22ddq6s16uajRu7xf2vHY0x99pr/cfd7Za1VlXffKPqtWuVM268dvx0/9937q9/rdow87Na\nawPjU+y8+WYV/PnPciUkKLZ33Ty7RYWBsr1OOkmpp53qX6jbXzje8vLA46YS0er16+WrqVHJG2/I\nU1iosnffU8648Vp/+BHKGTdeZUuWtO1ARJhn3z6tO+xw7f3739u8bc26dSpfskRbpk47IHuhkbhG\nWMKokYHHu2fNalcdgT9W7qmKer6KClV9/bXybrix0ai33vJy7fvXv+Srrg6sq87J0dbp01X8ctP3\ndjRkEhNDljefcWaLX0alb78t97ehP86t2y3P3tDuN+6d+a2OwynJDebhC/7S6glKXn9dG487PqQr\nVT3r8ag2L087fnGVcsaNV3Xd1eat51+gLWefI3d+vv8cXLNGm885R8X/+5+q1qxR+UcfqzZvZ5e+\njpxx47V5ytkh69wFBcq/fWabLpBYa1X83xdl3e6QVpTKL79UzrjxKl2wQNU5Oc3UECrcQCAxxjRa\nt+/vj4cs13dVc/VKafW+DhRlS5aoYtn+uW191dWNP5M8HlV8ulS127eHdMMunT9fOePGy72za89P\nyR9n3k03qXo93b7bwltaqup167p0n7Xbt2vHlT/XxuOOD1m/99G/tLhtTF1S1FD/mbeH3HrT3riK\nX3pJW6ZOU9UXX6g2NzfkInFDO2+5Vbv/+McO7bM9PHv3KveaX2vd+INV9dVXLZa31ja6f3THz36m\n8sX+BK/8ww+V+4urtO3i6SG9uupVLl2mzaeeFlj2lpc3agl35+Zq398fV+327Uo85BBJUvyIEZKk\n/r+9U4ljD5J71/7fUevGH+z/zJ8/P+TzpbTuXuOqNWu0/vAjlHv1rwItv/X/tp57ntYffoTyb5uh\njccep7wGFyzyfn2t9v7jH4HyBQ89FHjOV1PT6Rcl6scsCXc+W2ub/IxaN/GowOOajRu17uBDGpWp\nXr9Bhc8+q53/7xbljBuvimWfRSjq6BDbchG0Rfzo/d3Riue+rOK5/gTFJCXJlZAgb3Gx+lxzjXqd\n9H1tu+hiuXr10qhFCxWbmSnJfxWmdP6CQB22fhoH+VvH9v7t7+p/6y0y8fGBMiVvvKH822Zo+Msv\nK2mC/yS2brcqP/9cMVlZMvHxSqj7cOgM1ueT9XjkCoqpJd6SEtVs2aLEgw+WKyGh1dv5Kiu1fuJR\nSpo4UcPnNJ6jzXq9Kp2/QGlTzpJp5guq7N33lHfNNRrz0YeK7ds35LUU/vtZWbdbmVdcLk9BgeKH\nDJG3uFi7H3xQJS+/IpOYKBuUjAbqXLgw7L4KZt2vsV99KRmjredfIEn69utv9O2dv5UkZfxwujIv\nv0IJI5t4j8J8gG464URl/vSniundWybGpfRpoS2R+bfN0IjXQwczKnvvPdma0C+mbT/4gcauXKGa\nbduUMHq0ZIxMXFzgnKtXu22bKpYvl6+iUr0vuzTkvfaWlcnW1Ci2z/77aL2lpardulVJhx8uyZ/g\n+2prA1dZW8U2mLukzoZJR2vc6lUysc1/fPmqq2VraxWTliZfZaWKX3tNvS+9tNFrk6Sq1Wu09+9/\n16CHHpSrwYWCppS89ZaSDjlE8cOH+7/k3O6Qv8tG8VRVycTHy9R1kaqXP+N2SVLe1b+SJPU68USV\nf/CBhs15Qdsv+1HI+7912rka+q9nAnNFbzr5lJC6vr19Zsjy6PfeVdzAgYHl4M+TtrIejyo//1wJ\n48bJlZCgimXLFD9kiEreektZV14pyf9lvO/pZ5T505/IGKP8/3eLKj//XL7KSmXfP0uupCS58/O1\n759PqWbrFlUuXaa+N1yv2tw8eXbvVu327XI3uGqfM2684oYOlXvHDknSzhtv2v/6PvhAcf37+ePz\n+eSrrFRMr14h24c7T2Jc+49BcN3e8vLA9kUvvVT3wtt1uKJC1TffKG7IENVu3SpbXa2UY49V2bvv\nKuV73/P/nbfwN1SzebNcvXoprn9/VSz7zP95ExujvLpuaiYxUQN+e6e+/c2dkqRhzz0rX3WNUo4/\nLlDH5jPOlCSNX+e/2PDt7+/yx7ZqleIGDQqUq1j2marXrFbqGWcofvBgSdK+p55S4fMvaOA99yhx\n3NjAZ7WtrVXt9u0h3b8lKe+mm+SKT1D2/eEvGld99ZXK5i9Q9arVGr14/xye1evXq2bDRqVPPUcV\nS5eqcuUX6nvtr5s9NtZa1W7erG2XXqb+t92qjB/8oMmy1Tk5cu/apdSTTmq2zmi142dXqnr16sB7\n2JymPmOsz6f1R01S1k9/qr7XXydJKl20SEkTJii2f/+Qz0V3fn7gvGmPwY89pu2XXqqEMaPl2btP\n3roLZyYpSZnnn6+9f/1ru+uWpF133yNJ/s/nOgPvu08ZF5wfUs56PCp90z9icb8ZM5r9TdIatbm5\nKluyRL1/+EP/d43Pp6pvVknWp+Sj/AmNtVbbL71MVV9+Gdhu2w8v0bictTLGqOTNt5R60vflSkmR\nr6pKBQ8/rMwf/UibTz8jUD7l+ONV8fHHIfvOa2XX1L2PPyFXSkqzUxFJ+8cUiM3MDDmvMi44X7tW\nrQopu/Omm6Wbbg4s77r7HnkKC7X3L/73sb235gXfNrXvH/9U3JAhql6zNtBCPOL1/ylx7NhW1+fZ\nt0/f3vlb2ZoaDfrzI4pJTQ1brvjllwO//yT/xY1BDz4gyf8bdu/fH9fev/5VGRf9QMnfPUbp55wt\n63arYunSQO+5YDnjxkuSRn/4gaq++ko7r78h5PkdP/mJhj77b6V85zutfi3RzERzV4dJkybZFStW\nOB1G0+5KlyRtiovT+YMH6sHde3TGbbsCJ1Fb9L70UnmKClUWlLTWG/fN18q97jpVfNDyPa8ZF1+s\n4vofXEHqT9qiF19SzYYNqlm/XpVBxzamTx+NfON1yRhVrVyp2AED5UpJlnvHDqWccIK8+/ap/IMP\nVLpgoSo++ijsvgfc9XvVbNmi2k2bVZubK3dumLm6jAmbiAXquPtuJYwaqeRJk1Sbl6ed198gd35+\no4EYAtUlJCjzZz9V2fwFGvHqK1ofdDVq6L/+pbjBg5Q/43ZV1XUXbIpJTg77gSDJfy9GF7Z+97n2\nWiWMGaOU447VphO/79ik6EmTjlLViuaPW1vFjx4lV1KykidNUtLhhythzGjFpKfLejzyFhUpbuBA\nxWT4BzkrfPY57b7vPo3+8AOVLV6s3ffcG1JX2pSzFNM7U+78fFV8/LHiR42SiY/XgN/coYLZD6vy\nM/9Vxn633qKCBx4MG0/aOefIldpLxf/5b5Mx9//NbxQ/dIhyf3m14keOVGz/fqpcuqzJ8oHXOmyY\n3N9+q9RTT5WrV6/A32X2n+5X6imnyJWSoqpVq7TtootbdewiJf2881T+0UfqNXmysmf5WwJ23npb\n4Cp2MFdamjJ/dJn2/q3t3Zm6UtrZZ6v0rbckSaOXLJb1ehU3eLDKFi0KSXTPOs9/HgzNTNaHt/mT\niPoLf5L/b68+YcmfeYdKXntNsQMHylPXe2HUgvkyScmBZDlaWGv9V/CtlaxVbP/+Kp2/QLt+//uQ\nciknTFbFh+E/v1PPOlMD7/0/uZKTAj+w67/Lko48MuSHcHvFpKc3agEf9vxzSjzkEK0/cmKr60mZ\nPDnwPZR+/vlKm3KWEidM0MbvHRsoM/zF/wYunNUr//gT5f7854HlrKt/qb7XXqt1Ew4Nu5+M6dMV\nm5Wl+GFD/X/HKftb38N9z8cOHKihT/1T8YMHq/C551XwwAMhz2f+5Cfqf7v/XKtvqStbtEjeomKl\nnnqKkg4/vNmLX+3hq6mRiY3VxmOPk7ekRH2uuUaZV1yumPT0RmWttar68ivl33ab3Hl5yrjkhxow\nc6bWHeY/jinHfk9D/vGPRhff6rctf/995dXd4hSXna2YPn2Ucf55ql63vsXpzEbOf1sJI0aoNjdX\nm087vd2vd8iTTyjl+OO1Z/ZspZ93nmq3bw8kXdl/ul/p06a16zdaq/f/xONKGDOm0UXFeqmnnaqU\nE05Q5WfLA0ntmE8/CTReSP4LaNWrVyvx4IMVk5Ym6/Wqeu3aFr8rEg4er5q1reuNknHRDwINK06I\nHzFCo+a/3Wi9r7ZW6w87PMwWznClpemgpZ/KV1GhyuXL1eukkxqd/9bnk7xerTv0sJD1mVf+TP1v\nvVXe8nLtmf2w0qaeI3m92v6jH4fd16h3FnXo3G9O8jHHaNi/2j6lYlcxxqy01rZqImUS145oKnE9\n+BAmrgciZNQ7ixQ3YECjLwWgreoT1+FZyXr/Vn/i6t65U5tOOTVQpv7qf+nCRdp5ww0a9PBs/xX/\nIMPnzlXSoRO6KOrmWWsjOjANoldMRobihw2Tr6pKsf37K25QtuT1qWbjRsVkZspbVBSRCwzd2cg3\n5/l7DwVxFxRo3z//qX433CBXSorWTTxKiQcd1KoutOgcaVOnatADf2q03vp8Ybu/ouNa02PCKW1J\nXLnHtROMWrTI6RDQA/U6+WSnQ3CEr6pKJi7O6TDQzWUHtX65grsKB3VXlRQYHdMk+Fu+4uq6rQbL\nveqqzgixXWo2bnQ6BHQRb3Gxqr7+WjUbNqjio49U/N8XVTx3rqq++krl7757QCatadOmatSi/bfp\nxA8f3qhMXL9+GnDHHYEW81FvztOQJx6XSU4OlEloQ5fQA1nc4MFKOf54xQ4YoEGzH2p5gyYkHXlE\n2PXG5VLvSy9pd73NGfrsv5X8vWM6pe6uNODee5wOwVHc49oJ4gcParkQ0Eb9b7tV5e++2+btkidN\nCukW3t3U3y815qMPtXHyCQ5Hg+7K9u0raa//cTMdjXb/8Y8aeO+9+3vNGJfSz52mktffCJTxRtFo\n3Funnet0CECn6nPNr9Tn6qsDXZbrDZ/7kpIO9XfzHvv1VzIxMS3ety35uzFL0sjXXlXNpk3a98wz\n6nvd9fIUFCgmPU0FDz6kmg0bNODuu1X52TKVvj0/8i+qm0ibclbg9Q9/6UUlHRba88lbVt7oloSG\n+lxzjeIGDdK3v/lNYF3GBRfovfUFOnxwhjJTQrvH9//tb2V9PhX/t/nu5YHyd9yhtLOnqPyDD1W9\nepX6zZghd16efJVVMvFxKp77svrfMVPG5VLy0Ud3+x4qvS+6SL2OPTakp1BL0s/tOd8TtLh2kpFv\nv63Us84MDO1df9U+fvQoJ8NChGX94hddtq+4QYM0avHiNm+X/VDj+ztHhrm3JGrVJa7Bg2ihZxv6\n9FMavy5HfW++ueXCrbSwZP+Po617Q+8bzw7qslY892VZa2Xr7ms3Ma6wX/ptncKoM9gGo4ACPUna\nlCka+83X6nv99TLx8ep1iv++0WEvPK/x63ICSaskuRISWpW0BosfNkypp5yi4c8/r5TvfkfpU89R\nrxNO0Mg3Xtf4dTnqPf1iDZo9W4MeeURxQ4dq7BcrG02PNbCFQYicknraaS0XCpJ2zjka8s9/SvL/\nXu174w0at3qVBs2erez7ZylhzBglHtw44es9/WKNX5ejEf97LWy949flqO/11ynjwgs0fl2OBv/9\nbxr9wQcqszH66TOfa+K972jmq9+EbGOM0cC77tL4dTkat3pV4PN3zCcfK27Y0EC5rF/+UgetWKHM\ny3+s2KwsZVxwvgb87ndyJSQoYdQoJR06QYljx2rAnb8J3LdvjNG4nLXqd+styvrV1Rr1ziIlf/e7\nbTpWkdKrHYO19f+df1CnuEGDNH5djgbOannE6n4zZrSqXHfBPa4dEe4e1xs3S/Gtmz6hYvly7bj8\nijbtMv3cc1WxdKn63nSTko+aqLJFi9T7Rz9S4TPPaM+fH23zS+iIlGO/p4F/nKXyDz/Qrt/+rtmy\nwQOcRIOmBh6KychQ78t/rNisPo2uImb96mpl/vjHcu/cqeqcHPWaPDl0xFaPR6ULF8pbUqLU739f\ncdnZstZq8ymnyp3vn3bGJCRo9LtLtHvW/fJVViq2X99mBwaql37uNGXf75+vrvDf/w5Mvt5QyrHf\n06BH/6KNkyfLVlVJsbEav3qVKj//XLvu+6P6/voapRx3nFxJSfIUFcm9fbti+/ZVTGam5PPJxMWp\nYtky5d8+U97CwkCd6eedp14nnBAYWbkrjVv1TUhX4dodO0JGQOwJEg8/TIMfeURbL75Y3j17Gz0/\nbM4LiuvfP3CFNXbgQA3+8yMqnb9Ahc/4B1ww8fGBRGbYnBe0PcwE7h3R77bblPmTK+QrK5NcLtVs\n3OifysDnk6+iQtXr12vndddL8n+ptjT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iUXuvH47h9aXHPDQqCsW95KoN3891yoV4cCQU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\nRqQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ddb5470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotP()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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HgZMy85HRlypJmosWfNXRUAY9xfdxqtN3LwMuBx6gmiCxI3A/VYd1E3BcZl40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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e4cb320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(6, 6))\n",
    "im = plt.imshow(P, interpolation=\"none\", cmap=plt.get_cmap('binary'))\n",
    "plt.title('Covariance Matrix $P$ (after %i Filter Steps)' % (m))\n",
    "ylocs, ylabels = plt.yticks()\n",
    "# set the locations of the yticks\n",
    "plt.yticks(np.arange(6))\n",
    "# set the locations and labels of the yticks\n",
    "plt.yticks(np.arange(5),('$x$', '$y$', '$\\psi$', '$v$', '$\\dot \\psi$'), fontsize=22)\n",
    "\n",
    "xlocs, xlabels = plt.xticks()\n",
    "# set the locations of the yticks\n",
    "plt.xticks(np.arange(6))\n",
    "# set the locations and labels of the yticks\n",
    "plt.xticks(np.arange(5),('$x$', '$y$', '$\\psi$', '$v$', '$\\dot \\psi$'), fontsize=22)\n",
    "\n",
    "plt.xlim([-0.5,4.5])\n",
    "plt.ylim([4.5, -0.5])\n",
    "\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "divider = make_axes_locatable(plt.gca())\n",
    "cax = divider.append_axes(\"right\", \"5%\", pad=\"3%\")\n",
    "plt.colorbar(im, cax=cax)\n",
    "\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## State Vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "def plotx():\n",
    "    fig = plt.figure(figsize=(16,16))\n",
    "\n",
    "    plt.subplot(311)\n",
    "    plt.step(range(len(measurements[0])),x0-mx[0], label='$x$')\n",
    "    plt.step(range(len(measurements[0])),x1-my[0], label='$y$')\n",
    "\n",
    "    plt.title('Extended Kalman Filter State Estimates (State Vector $x$)')\n",
    "    plt.legend(loc='best',prop={'size':22})\n",
    "    plt.ylabel('Position (relative to start) [m]')\n",
    "\n",
    "    plt.subplot(312)\n",
    "    plt.step(range(len(measurements[0])),x2, label='$\\psi$')\n",
    "    plt.step(range(len(measurements[0])),\n",
    "             (course/180.0*np.pi+np.pi)%(2.0*np.pi) - np.pi,\n",
    "             label='$\\psi$ (from GPS as reference)')\n",
    "    plt.ylabel('Course')\n",
    "    plt.legend(loc='best',prop={'size':16})\n",
    "\n",
    "    plt.subplot(313)\n",
    "    plt.step(range(len(measurements[0])),x3, label='$v$')\n",
    "    plt.step(range(len(measurements[0])),speed/3.6,\n",
    "             label='$v$ (from GPS as reference)')\n",
    "    plt.ylabel('Velocity')\n",
    "    #plt.ylim([0, 30])\n",
    "    plt.legend(loc='best',prop={'size':16})\n",
    "    plt.xlabel('Filter Step')\n",
    "\n",
    "    plt.savefig('Extended-Kalman-Filter-CHCV-State-Estimates.png',\n",
    "                dpi=72, transparent=True, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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r1oz777/fq2h/UdhCt7ZzrnWRJhERkYBxw7tzWbfrIIN6NOTyDrW9jiMB6sI2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\nke8yOnnXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a17a775c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotx()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Position x/y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "#%pylab --no-import-all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "def plotxy():\n",
    "\n",
    "    fig = plt.figure(figsize=(16,9))\n",
    "\n",
    "    # EKF State\n",
    "    plt.quiver(x0,x1,np.cos(x2), np.sin(x2), color='#94C600',\n",
    "               units='xy', width=0.05, scale=0.5)\n",
    "    plt.plot(x0,x1, label='EKF Position', c='k', lw=5)\n",
    "\n",
    "    # Measurements\n",
    "    plt.scatter(mx[::5],my[::5], s=50, label='GPS Measurements', marker='+')\n",
    "    #cbar=plt.colorbar(ticks=np.arange(20))\n",
    "    #cbar.ax.set_ylabel(u'EPE', rotation=270)\n",
    "    #cbar.ax.set_xlabel(u'm')\n",
    "\n",
    "    # Start/Goal\n",
    "    plt.scatter(x0[0],x1[0], s=60, label='Start', c='g')\n",
    "    plt.scatter(x0[-1],x1[-1], s=60, label='Goal', c='r')\n",
    "\n",
    "    plt.xlabel('X [m]')\n",
    "    plt.ylabel('Y [m]')\n",
    "    plt.title('Position')\n",
    "    plt.legend(loc='best')\n",
    "    plt.axis('equal')\n",
    "    #plt.tight_layout()\n",
    "\n",
    "    plt.savefig('Extended-Kalman-Filter-CHCV-Position.png',\n",
    "                dpi=72, transparent=True, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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E9L4/7ba8cal4E27+N5eplZBvxKwRHWnf4156vvk9IU+/g1/N+rmOj9u7kSXv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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1856d780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotxy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Detailed View"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "def plotxydetails():\n",
    "    fig = plt.figure(figsize=(12,9))\n",
    "\n",
    "    plt.subplot(221)\n",
    "    # EKF State\n",
    "    #plt.quiver(x0,x1,np.cos(x2), np.sin(x2), color='#94C600', units='xy', width=0.05, scale=0.5)\n",
    "    plt.plot(x0,x1, label='EKF Position', c='g', lw=5)\n",
    "\n",
    "    # Measurements\n",
    "    plt.scatter(mx[::5],my[::5], s=50, label='GPS Measurements', alpha=0.5, marker='+')\n",
    "    #cbar=plt.colorbar(ticks=np.arange(20))\n",
    "    #cbar.ax.set_ylabel(u'EPE', rotation=270)\n",
    "    #cbar.ax.set_xlabel(u'm')\n",
    "\n",
    "    plt.xlabel('X [m]')\n",
    "    plt.xlim(70, 130)\n",
    "    plt.ylabel('Y [m]')\n",
    "    plt.ylim(140, 200)\n",
    "    plt.title('Position')\n",
    "    plt.legend(loc='best')\n",
    "\n",
    "\n",
    "    plt.subplot(222)\n",
    "\n",
    "    # EKF State\n",
    "    #plt.quiver(x0,x1,np.cos(x2), np.sin(x2), color='#94C600', units='xy', width=0.05, scale=0.5)\n",
    "    plt.plot(x0,x1, label='EKF Position', c='g', lw=5)\n",
    "\n",
    "    # Measurements\n",
    "    plt.scatter(mx[::5],my[::5], s=50, label='GPS Measurements', alpha=0.5, marker='+')\n",
    "    #cbar=plt.colorbar(ticks=np.arange(20))\n",
    "    #cbar.ax.set_ylabel(u'EPE', rotation=270)\n",
    "    #cbar.ax.set_xlabel(u'm')\n",
    "\n",
    "    plt.xlabel('X [m]')\n",
    "    plt.xlim(160, 260)\n",
    "    plt.ylabel('Y [m]')\n",
    "    plt.ylim(110, 160)\n",
    "    plt.title('Position')\n",
    "    plt.legend(loc='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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WQU6H/czFyL9J1JlPPbj+zHrWn1lPx6odmd5hOs0rNc/ZhIUQ+ca6U5u5fPeU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\nlItp0q2UUkoppZSLadKtlFJKKaWUi/0/JDmJiyKCaXYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ddc4080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotxydetails()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "As you can see, complicated analytic calculation of the Jacobian Matrices, but it works pretty well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "## Write Google Earth KML"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### Convert back from Meters to Lat/Lon (WGS84)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "latekf = latitude[0] + np.divide(x1,arc)\n",
    "lonekf = longitude[0]+ np.divide(x0,np.multiply(arc,np.cos(latitude*np.pi/180.0)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### Create Data for KML Path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "Coordinates and timestamps to be used to locate the car model in time and space\n",
    "The value can be expressed as yyyy-mm-ddThh:mm:sszzzzzz, where T is the separator between the date and the time, and the time zone is either Z (for UTC) or zzzzzz, which represents ±hh:mm in relation to UTC."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "import datetime\n",
    "car={}\n",
    "car['when']=[]\n",
    "car['coord']=[]\n",
    "car['gps']=[]\n",
    "for i in range(len(millis)):\n",
    "    d=datetime.datetime.fromtimestamp(millis[i]/1000.0)\n",
    "    car[\"when\"].append(d.strftime(\"%Y-%m-%dT%H:%M:%SZ\"))\n",
    "    car[\"coord\"].append((lonekf[i], latekf[i], 0))\n",
    "    car[\"gps\"].append((longitude[i], latitude[i], 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'simplekml'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-31-724705907364>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0msimplekml\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mKml\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mModel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mAltitudeMode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mOrientation\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mScale\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mStyle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mColor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'simplekml'"
     ]
    }
   ],
   "source": [
    "from simplekml import Kml, Model, AltitudeMode, Orientation, Scale, Style, Color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "# The model path and scale variables\n",
    "car_dae = r'https://raw.githubusercontent.com/balzer82/Kalman/master/car-model.dae'\n",
    "car_scale = 1.0\n",
    "\n",
    "# Create the KML document\n",
    "kml = Kml(name=d.strftime(\"%Y-%m-%d %H:%M\"), open=1)\n",
    "\n",
    "# Create the model\n",
    "model_car = Model(altitudemode=AltitudeMode.clamptoground,\n",
    "                            orientation=Orientation(heading=75.0),\n",
    "                            scale=Scale(x=car_scale, y=car_scale, z=car_scale))\n",
    "\n",
    "# Create the track\n",
    "trk = kml.newgxtrack(name=\"EKF\", altitudemode=AltitudeMode.clamptoground,\n",
    "                     description=\"State Estimation from Extended Kalman Filter with CHCV Model\")\n",
    "\n",
    "# Attach the model to the track\n",
    "trk.model = model_car\n",
    "trk.model.link.href = car_dae\n",
    "\n",
    "# Add all the information to the track\n",
    "trk.newwhen(car[\"when\"])\n",
    "trk.newgxcoord(car[\"coord\"])\n",
    "\n",
    "# Style of the Track\n",
    "trk.iconstyle.icon.href = \"\"\n",
    "trk.labelstyle.scale = 1\n",
    "trk.linestyle.width = 4\n",
    "trk.linestyle.color = '7fff0000'\n",
    "\n",
    "# Add GPS measurement marker\n",
    "fol = kml.newfolder(name=\"GPS Measurements\")\n",
    "sharedstyle = Style()\n",
    "sharedstyle.iconstyle.icon.href = 'http://maps.google.com/mapfiles/kml/shapes/placemark_circle.png'\n",
    "\n",
    "for m in range(len(latitude)):\n",
    "    if GPS[m]:\n",
    "        pnt = fol.newpoint(coords = [(longitude[m],latitude[m])])\n",
    "        pnt.style = sharedstyle\n",
    "\n",
    "# Saving\n",
    "#kml.save(\"Extended-Kalman-Filter-CTRV.kml\")\n",
    "kml.savekmz(\"Extended-Kalman-Filter-CHCV.kmz\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "print('Exported KMZ File for Google Earth')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "To use this notebook as a presentation type:\n",
    "\n",
    "`jupyter-nbconvert --to slides Extended-Kalman-Filter-CHCV.ipynb --reveal-prefix=reveal.js --post serve` \n",
    "\n",
    "Questions? [@Balzer82](https://twitter.com/balzer82)"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  },
  "latex_metadata": {
   "author": "Paul Balzer"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
